The first environment used in our experiments is a Minecraft inspired 2D crafting world [1]}.
The world has a discrete state and action. The agent is able to move, pick up or drop off certain items present in the map, as well as perform actions on those items.
With this environment, we can define tasks such as chop tree, break rock, make bread, build house, etc. and combine them into sequences such as [make bread, eat bread, chop tree, build house]. This provides a good selection of unique tasks and sequences to generate training data.
As detailed in the methodology section, the objectives of the agent are specified implicitly by providing two trajectories consisting of observations of both the current trajectory, and the reference trajectory.
The advantage of this approach is that no explicit ordering of subtasks is specified, and the agent is free to execute tasks in the most appropriate manner.
Our framework is trained with randomly generated starting environments and random combinations of tasks to complete.
The complexity of the problem increases as more tasks are required to reach the target end state. The previous state-of-the-art approach in this environment [2]} used the same input observations and expert reference trajectories.
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